Cognitive complexity

Cognitive complexity
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Cognitive psychology
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Cognitive complexity describes cognition along a simplicity-complexity axis. It is the subject of academic study in fields including personal construct psychology,[1] organisational theory[2] and human-computer interaction.[3]



First proposed by James Bieri in 1955.[1]

In psychology

Cognitive complexity is a psychological characteristic or psychological variable that indicates how complex or simple is the frame and perceptual skill of a person. A person who is measured high on cognitive complexity tends to perceive nuances and subtle differences which a person with a lower measure, indicating a less complex cognitive structure for the task or activity, does not.

an aspect of a person's cognitive functioning which at one end is defined by the use of many constructs with many relationships to one another (complexity) and at the other end by the use of few constructs with limited relationships to one another (simplicity)

Lawrence Pervin, Personality[3]

It is used as part of one of the several variations of the viable non-empirical evaluation model GOMS (Goals, Operators, Methods, and Selection rules); in particular the GOMS/CCT methodology.

Cognitive complexity can have various meanings:

  • the number of mental structures we use, how abstract they are, and how elaborately they interact to shape our perceptions.
  • "an individual-difference variable associated with a broad range of communication skills and related abilities ... [which] indexes the degree of differentiation, articulation, and integration within a cognitive system".[4]

In computer science

In human-computer interaction, cognitive (or psychological) complexity distinguishes human factors (related to psychology and human cognition) from, for example, computational complexity.[5]

In artificial intelligence

In an attempt to explain how humans perceive relevance, Cognitive complexity is defined as an extension of the notion of Kolmogorov complexity. It amounts to the length of the shortest description available to the observer. Here is an example : Individuating a particular Inuit woman among one hundred people is simpler in a village in Congo rather than in an Inuit village.

Cognitive complexity is related to probability (see Simplicity theory): situation are cognitively improbable if they are simpler to describe than to generate. Human individuals attach two complexity values to events:

  • description complexity (see above definition)
  • generation complexity: the size of the minimum set of parameter values than the 'world' (as imagined by the observer) needs to generate the event.

To 'generate' an event such as an encounter with an Inuit woman in Congo, one must add up the complexity of each event in the causal chain that brought her there. The significant gap between both complexities (hard to produce, easy to describe) makes the encounter improbable and thus narratable.

See also


  1. ^ a b Bell, R.C. (2004-02-14). "Cognitive complexity". The Internet Encyclopaedia of Personal Construct Psychology. The Psychology of Personal Constructs. Retrieved 2010-04-30. 
  2. ^ Villanova University. "Analyzing Organizations Through Cognitive Complexity". Retrieved 29 April 2010. 
  3. ^ a b Rauterberg, Matthias (1996). "How to Measure Cognitive Complexity in Human-Computer Interaction". In Robert Trappl(ed). Proceedings of the Thirteenth European Meeting on Cybernetics and Systems Research. II. Thirteenth European Meeting on Cybernetics and Systems Research. University of Vienna, Austria. pp. 815–820. ISBN 3852061334. 
  4. ^ Burleson, B.R., & Caplan, S.E. (1998), "Cognitive complexity". In J.C. McCroskey, J.A. Daly, M.M. Martin, & M.J. Beatty (Eds.), Communication and personality: Trait perspectives (233–286). Creskill, NJ: Hampton Press
  5. ^ Thomas, John C.; Richards, John T. (2008). "Achieving Psychological Simplicity: Methods And Measures To Reduce Cognitive Complexity". In Sears, Andrew; Jacko, Julie A.. The human-computer interaction handbook: Fundamentals, Evolving Technologies and Emerging Applications (2nd ed.). Mahwah, New Jersey: Lawrence Erlbaum Associates. pp. 498–507. ISBN 9780805858709. 

External links

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